SBIR-STTR Award

Evolving and Certifiable Autopilot for Unmanned Aerial Systems
Award last edited on: 3/27/2023

Sponsored Program
SBIR
Awarding Agency
NASA : AFRC
Total Award Amount
$874,953
Award Phase
2
Solicitation Topic Code
A2.02
Principal Investigator
William A J Anemaat

Company Information

Design Analysis & Research Corporation (AKA: DAR Corporation)

1440 Wakarusa Drive Suite 500
Lawrence, KS 66049
   (785) 832-0434
   anemaat@darcorp.com
   www.darcorp.com
Location: Single
Congr. District: 01
County: Douglas

Phase I

Contract Number: 80NSSC18P1909
Start Date: 7/27/2018    Completed: 2/15/2019
Phase I year
2018
Phase I Amount
$124,959
The project consist of the development of a new intelligent flight control system with learning capabilities and a high degree of assurance, that can be certified by the FAA Machine learning and artificial intelligent research has led to many tangible results and recent developments in cognitive control and decision making. Although automatic flight controllers are widely used and they have become common in recent years, they often lack intelligence, adaptability, and high performance. Reliability of UASs in unforeseen conditions is a direct function of their intelligence and adaptability. The proposed project aims to take advantage of high-performance computing platforms and the state-of-the art machine learning and verification algorithms to develop a new intelligent, adaptable, and certifiable flight control system with learning capabilities. The autopilot system will be able to learn from each flight experience and develop intuition to adapt to a high level of uncertainties. To provide a high degree of assurance and to make the learning autopilot system safe and certifiable, a secondary and conventional autopilot system will be integrated based on the run-time assurance architecture. A monitor will be developed to continuously check aircraft states and envelope protection limits, and handover aircraft control to the conventional autopilot system if needed. Provable guarantees of the monitor and the controllers will be provided using formal analysis. The propose a hybrid flight control system which has adaptability and intelligence of skilled pilots and at the same is cable of performing complex analysis and decision making algorithms in real-time. We aim to build and train an artificial neural network model that can mimic the performance of the classical robust optimal controllers, extend the robustness, adaptability, and curiosity of the artificial neural network controller and integrate a Real-Time Assurance (RTA) system. Potential NASA Applications The autopilots could be used on many of NASA's currently flying UAS's and newly developed systems. Potential Non-NASA Applications The autopilot can be used on any commercially and military available UAS system.

Phase II

Contract Number: 80NSSC19C0102
Start Date: 8/14/2019    Completed: 8/13/2021
Phase II year
2019
Phase II Amount
$749,994
An intelligent flight control system is developed with learning capabilities and a high degree of assurance that can be certified by the FAA and tested on a modular reconfigurable UAS. Existing lack of intelligence, adaptability and high performance of current automatic flight controllers is addressed by taking advantage of high-performance computing platforms, state-of-the-art machine learning and verification algorithms to develop a new intelligent, adaptable and certifiable flight control system with learning capabilities. The autopilot system will be able to learn from flight experience and develop intuition to adapt to a high level of uncertainties. To provide a high degree of assurance and make the learning autopilot system safe and certifiable, a conventional autopilot system is integrated based on a run-time assurance architecture. A monitor is developed to check aircraft states and envelope protection limits and handover aircraft control to a conventional autopilot system if needed. Provable guarantees of the monitor and the controllers is provided using formal analysis. The hybrid flight control system has adaptability and intelligence of skilled pilots and is capable of performing complex analysis and decision making in real-time. An artificial neural network model is built and trained to mimic the performance of classical robust optimal controllers, extending robustness, adaptability and curiosity of artificial neural network controllers and integrating a Real-Time Assurance system. Technology demonstration of the intelligent flight control system is achieved by flight testing of a Modular Air Vehicle, where the configuration can be customized to fit flight test needs and test adaptability of the proposed technology. A Modular Air Vehicle is designed and prototyped. Once the intelligent flight controllers are integrated with the airframe, ground and flight tests will be carried out to verify the performance and reliability of the proposed technology. Potential NASA Applications (Limit 1500 characters, approximately 150 words) The developed autopilot could be used on many of NASA UASs and newly developed aircraft, such as the X-57 and the GL-10 Greased Lightning. The Modular Air Vehicle can be used for all sorts of research projects since it is very easy to change the configuration for any of NASA needs for flight test research, VTOL research, autopilot research, system failure research, etc. Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words) The autopilot can be used on any commercially and military available UAS system. The Modular Air Vehicle will be commercialized for use at universities and other research institutes world-wide for flying research on different type of unmanned vehicles. A smaller version will be developed for science and technology classes in high schools.